Clinically deployed segmentation models are known to fail on data outside of their training distribution. As these models perform well on most cases, it is imperative to detect out-of-distribution (OOD) images at inference to protect against automation bias. This work applies the Mahalanobis distance post hoc to the bottleneck features of a Swin UNETR model that segments the liver on T1-weighted magnetic resonance imaging. By reducing the dimensions of the bottleneck features with principal component analysis, OOD images were detected with high performance and minimal computational load.

翻译：临床上部署的分割模型已知在处理训练分布之外的数据时会失效。由于这些模型在大多数情况下表现良好，因此必须在推理过程中检测分布外（OOD）图像，以防止自动化偏差。本研究将马氏距离事后应用于Swin UNETR模型的瓶颈特征，该模型用于在T1加权磁共振成像上分割肝脏。通过使用主成分分析降低瓶颈特征的维度，OOD图像得以高效检测，且计算负荷极低。